Numpy is not inserting the right array into multidimensional array

Question

I have a matrix

M = np.array([
[1, -2, -2, -2, 1, 2],
[0, 3, -2, -3, 1, 3],
[3, 0, 0, 1, -1, 2],
[3, -3, -2, 0, 1, 1],
[0, -3, 3, -3, -3, 2]
])

and I'm trying to replace the first row by itself modulo some number N = 2497969412496091.

I've been playing around with this in the IDE for a while, and even though

>>> M[0] % N
           
array([1, 2497969412496089, 2497969412496089, 2497969412496089, 1, 2], dtype=int64)

After I preform M[0] = M[0] % N and print the matrix M, I get

>>> M[0]
           
array([1, -746726695, -746726695, -746726695, 1, 2])

I've also tried to copy the intermediate step M[0] % N in a temporary variable and then setting it equal to M[0] but the problem still persists. What is going on here?

Patrick Artner · Accepted Answer · 2020-08-26 19:18:31Z

1

Your array is np.int32:

print(type(M[0][0]))  # <class 'numpy.int32'>

Create the original array as np.int64 - to avoid getting integer overflow happening:

import numpy as np

M = np.array([
[1, -2, -2, -2, 1, 2],
[0, 3, -2, -3, 1, 3],
[3, 0, 0, 1, -1, 2],
[3, -3, -2, 0, 1, 1],
[0, -3, 3, -3, -3, 2]
], dtype=np.int64)

N = 2497969412496091
M[0] = M[0] % N

print(M)

Output:

[[ 1 2497969412496089 2497969412496089 2497969412496089       1            2]
 [ 0                3               -2               -3       1            3]
 [ 3                0                0                1      -1            2]
 [ 3               -3               -2                0       1            1]
 [ 0               -3                3               -3      -3            2]]

answered Aug 26, 2020 at 19:18

Patrick Artner

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2 Comments

LightsOutTorus Over a year ago

Thank you, it seems to work fine after specifying dtype = np.int64. I just read the documentation for numpy.array and it says the default datatype is based on the minimum type required to hold the object, but how come it outputs the correct answer when I just do M[0] % N, even showing dtype = int64?

Patrick Artner Over a year ago

@Lig because you create a new array with numbers in it that do not fit int32 so it creates it as int64. Reasigning that to your old array does not change its datatype hence integer overflow

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